I am a PhD student at Fondazione Bruno Kessler (FBK) and European Laboratory for Learning and Intelligent Systems (ELLIS), under the supervision of Bruno Lepri (FBK), Andrea Passerini (University of Trento) and Manuel Gomez Rodriguez (Max Planck Institute for Software Systems). My research interests relate to trustworthy and explainable AI topics, focusing on algorithmic recourse, causality, counterfactual explanations and user-aware decision making systems.
During my PhD, I also spent a visiting period at the Max Planck Institute for Software Systems (Germany) and I interned at X, the moonshot factory (Google X) (Mountain View, CA) where I worked on Large Language Models (LLMs) applied to program synthesis supervised by Dr. Rishabh Singh.
Before the PhD, I was a Research Scientist at VUI, Inc., a Boston's startup building innovative conversational agents and a Research Assistant in the Structured Machine Learning Group (SML) at the University of Trento, Italy.
During my undergraduate studies, I interned at CERN (2019), and I spent a semester at the University of Edinburgh (2018) as an Erasmus student. I also participated in the Google Summer of Code (2017) as a Software Developer for Shogun, a machine learning library.
I received my Bachelor and Master's degree (cum laude) from the University of Trento in 2017 and 2020 respectively. I also obtained a scholarship for my academic performance through my undergraduate studies.
Latest News
- 10/2024: We realesed a new preprint "Time Can Invalidate Algorithmic Recourse" (with S. Teso, B. Lepri, and A. Passerini).
- 10/2024: "Towards Human-AI Complementarity with Predictions Sets" (with N. Okati, S. Thejaswi, E. Straitouri and M. Gomez-Rodriguez) was accepted at NeurIPS 2024! See you in Vancouver!
- 05/2024: I was invited as a panelist (with Nicola Dall'Asen and Prof. Roberto Caso) at the Co.Scienza Festival 2024 to discuss about ethics and artificial intelligence!
- 04/2024: I was invited at the European Centre for Algorithmic Transparency (ECAT) to give a talk about our work on algorithmic contestability and algorithmic recourse.
- 03/2024: Preference Elicitation in Interactive and User-centered Algorithmic Recourse: An Initial Exploration (with S. Esfahani, B. Lepri, A. Passerini, K. Tentori and M. Zancanaro) has been accepted to ACM UMAP (2024)!
- 01/2024: Personalized Algorithmic Recourse with Preference Elicitation (with P. Viappiani, S. Teso, B. Lepri and A. Passerini) has been accepted on Transactions on Machine Learning Research!
- 10/2023: This fall/winter I will be interning at Max Planck Institute for Software Systems, Kaiserslautern, Germany, under the supervision of Dr. Manuel Gomez Rodriguez working on human-centric machine learning research!
- For the older news, click here
Publications & Preprints
- Time Can Invalidate Algorithmic Recourse
Giovanni De Toni, Stefano Testo, Bruno Lepri, Andrea Passerini
Preprint (2024)
[paper][code]
- Towards Human-AI Complementarity with Predictions Sets
Giovanni De Toni, Nastaran Okati, Suhas Thejaswi, Eleni Straitouri, Manuel Gomez-Rodriguez
NeurIPS (2024)
[paper][code]
- Preference Elicitation in Interactive and User-centered Algorithmic Recourse: an Initial Exploration
Seyedehdelaram Esfahani, Giovanni De Toni, Bruno Lepri, Andrea Passerini, Katya Tentori, Massimo Zancanaro
ACM UMAP (2024)
Best Short Paper Runner-up at the 32nd ACM UMAP Conference (2024)
[paper][code (will be available soon)]
- Personalized Algorithmic Recourse with Preference Elicitation
Giovanni De Toni, Paolo Viappiani, Stefano Teso, Bruno Lepri, Andrea Passerini
Transactions on Machine Learning Research (2024)
[paper][code]
A preliminary version of this work was accepted at the at NeurIPS 2022 workshop on Human in the Loop Learning (HILL).
See here for the previous paper.
- Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis
Giovanni De Toni, Bruno Lepri, Andrea Passerini
Machine Learning (2023)
[paper][code]
- Learning compositional programs with arguments and sampling
Giovanni De Toni, Luca Erculiani, Andrea Passerini
Advances in Programming Languages and Neurosymbolic Systems (AIPLANS), NeurIPS, 2021.
10th International Workshop on Statistical Relational AI (StarAI), IJCLR, 2021.
[paper][code][poster]
- A general method for estimating the prevalence of Influenza-Like-Symptoms with Wikipedia data
Giovanni De Toni, Cristian Consonni, Alberto Montresor
PLOS ONE, 2021.
[paper][code]
- Pyglmnet: Python implementation of elastic-net regularized generalized linear models
Mainak Jas, Titipat Achakulvisut, Aid Idrizović, Daniel Acuna, Matthew Antalek, Vinicius Marques, Tommy Odland, Ravi Prakash Garg, Mayank Agrawal, Yu Umegaki, Peter Foley, Hugo Fernandes, Drew Harris, Beibin Li, Olivier Pieters, Scott Otterson, Giovanni De Toni, Chris Rodgers, Eva Dyer, Matti Hamalainen, Konrad Kording, Pavan Ramkumar
Journal of Open Source Software (JOSS), 2020.
[paper][code]
Teaching